Artificial Intelligence Mediated Discovery and Bio-validation of novel regulators of nervous system stability.

人工智能介导的神经系统稳定性新型调节剂的发现和生物验证。

• 批准号: 2444988
• 金额: --
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2444988
• 关键词: Artificial; Intelligence; Mediated; Discovery; Bio

The objective of this industrial case studentship is to provide multidisciplinary training in machine learned data-integration leading to the identification and bio-validation of factors capable of regulating homeostatic maintenance of the nervous system.There are publicly accessible and privately held research information resources whose artificial intelligence mediated mining promises to plug knowledge gaps surrounding the regulation of nervous system stability and in turn (outwith the scope of this studentship), development of novel stratified and precision regenerative medicines. This industrial case studentship combining training from Massive Analytic and access to state of the art platforms, with an interdisciplinary supervisory academic team will develop machine learned data-integration leading to the identification (and subsequent bio-validation) of regulators of nervous system development, homeostatic maintenance and postnatal ageing. Such factors would be future foci of broadly applicable therapeutic interventions to improve age related health span and response to degeneration inducing stimuli. This will be achieved through a unique academic/industrial collaboration to integrate commercially leading artificial intelligence platforms for multimodal data analysis provided by an industrial partner, Massive Analytic (https://www.massiveanalytic.com) with multidisciplinary academic human and animal model expertise in neuronal loss and dysfunction, pluripotent stem cell technology, and medical centric informatics and machine learning.Loss and dysfunction of brain region selective neuronal synapses is a characteristic feature of "normal healthy" ageing as well as all congenital and adult onset neurological disorders. Elucidating how synaptic alterations associated with advancing-age and/or genetic perturbations leave a neuron vulnerable is essential for our understanding of what regulates nervous system stability in health and disease, but also to the development and design of neuroprotective strategies.

这个工业案例研究的目的是提供机器学习数据集成的多学科培训，从而识别和生物验证能够调节神经系统稳态维持的因素。有公开访问和私人持有的研究信息资源，其人工智能介导的挖掘有望填补围绕神经系统稳定性调节的知识空白，并反过来（超出本研究范围），开发新型分层和精确再生药物。这个工业案例研究将Massive Analytic的培训与最先进的平台相结合，与跨学科的监督学术团队一起开发机器学习数据集成，从而识别（以及随后的生物验证）神经系统发育，稳态维持和产后衰老的调节器。这些因素将是未来广泛适用的治疗干预的焦点，以改善与年龄相关的健康范围和对变性诱导刺激的反应。这将通过独特的学术/工业合作来实现，将工业合作伙伴Massive Analytic（https：//www.example.com）提供的用于多模态数据分析的商业领先人工智能平台www.massiveanalytic.com与神经元损失和功能障碍，多能干细胞技术，以及以医学为中心的信息学和机器学习。大脑区域选择性神经元突触的丢失和功能障碍是“正常健康”的特征。衰老以及所有先天性和成人发病的神经系统疾病。阐明与年龄增长和/或遗传扰动相关的突触改变如何使神经元变得脆弱，对于我们理解在健康和疾病中调节神经系统稳定性的因素至关重要，而且对于神经保护策略的开发和设计也至关重要。